基于无代码AutoML的以人为中心的AI产品原型设计：概念框架、潜力与局限性
Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations
AI总结：
本文提出基于设计科学研究的以人为中心AI产品原型设计概念框架，通过无代码AutoML降低非专家参与门槛，并结合案例研究与标准分析验证其效用与局限。
摘要：
本文针对AI产品原型设计中固有的复杂性，重点关注AI行为的概率特性所带来的挑战，以及非专家用户对原型设计工具的可及性有限这一问题。本文提出了一种设计科学研究（DSR）方法，最终形成一个旨在改进AI原型设计流程的概念框架。通过全面的文献综述，识别了关键挑战，并将无代码AutoML作为一种解决方案进行了分析。该框架描述了在原型设计过程中无缝纳入非专家输入与评估的机制，利用无代码AutoML的潜力来增强可及性和可解释性。采用自然主义评估方法（案例研究）与人工评估方法（基于标准的分析）相结合的混合方法验证了本方法的效用，突出了其在支持AI非专家用户和简化决策方面的有效性及其局限性。本文还讨论了该方法对学术界和工业界的启示，强调将无代码AutoML进行战略性整合，以改进AI产品开发流程、降低风险并促进创新。
英文摘要：
This paper addresses the complexities inherent in AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to non-experts. A Design Science Research (DSR) approach is presented which culminates in a conceptual framework aimed at improving the AI prototyping process. Through a comprehensive literature review, key challenges were identified and no-code AutoML was analyzed as a solution. The framework describes the seamless incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability. A hybrid approach of combining naturalistic (case study) and artificial evaluation methods (criteria-based analysis) validated the utility of our approach, highlighting its efficacy in supporting AI non-experts and streamlining decision-making and its limitations. Implications for academia and industry, emphasizing the strategic integration of no-code AutoML to enhance AI product development processes, mitigate risks, and foster innovation, are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
